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Published on: January 10, 2014
How to make causal inferences using texts
Naoki Egami1, Christian J Fong2, Justin Grimmer3,4
1Department of Political Science, Columbia University, New York, NY 10027, USA.
Abstract:
Text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories with large collections of text. Nearly all text-based causal inferences depend on a latent representation of the text, but we show that estimating this latent representation from the data creates underacknowledged risks: we may introduce an identification problem or overfit. To address these risks, we introduce a split-sample workflow for making rigorous causal inferences with discovered measures as treatments or outcomes. We then apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.
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